R-AGNO-RPN: A LIDAR-Camera Region Deep Network for Resolution-Agnostic Detection

Understand

Current neural networks-based object detection approaches processing LiDAR point clouds are generally trained from one kind of LiDAR sensors.

• However, their performances decrease when they are tested with data coming from a different LiDAR sensor than the one used for training, i.e., with a different point cloud resolution.
• In this paper, R-AGNO-RPN, a region proposal network built on fusion of 3D point clouds and RGB images is proposed for 3D object detection regardless of point cloud resolution.
• As our approach is designed to be also applied on low point cloud resolutions, the proposed method focuses on object localization instead of estimating refined boxes on reduced data.